2017 INTERSPEECH INTERSPEECH 2017

Phonological Markers of Oxytocin and MDMA Ingestion

Abstract

Speech data has the potential to become a powerful tool to provide quantitative information about emotion beyond that achieved by subjective assessments. Based on this concept, we investigate the use of speech to identify effects in subjects under the influence of two different drugs: Oxytocin (OT) and 3,4-methylenedioxymethamphetamine (MDMA), also known as ecstasy. We extract a set of informative phonological features that can characterize emotion. Then, we perform classification to detect if the subject is under the influence of a drug. Our best results show low error rates of 13% and 17% for the subject classification of OT and MDMA vs. placebo, respectively. We also analyze the performance of the features to differentiate the two levels of MDMA doses, obtaining an error rate of 19%. The results indicate that subtle emotional changes can be detected in the context of drug use.

🌱 Topic Pioneer β€” Emotion Recognition
πŸŒ‰ Interdisciplinary Bridge β€” Computer Vision and Machine Learning and Natural Language Processing
πŸ“ˆ Trend Setter β€” Audio Processing
🧭 Keyword Pioneer β€” drug detection
🐣 Hot Topic Early Bird β€” multi-class classification
🐝 Cross-Pollinator β€” Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio